{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T14:52:10Z","timestamp":1740149530802,"version":"3.37.3"},"reference-count":36,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,6]],"date-time":"2022-07-06T00:00:00Z","timestamp":1657065600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"An integrated navigation algorithm based on a multiple fading factors Kalman filter (MFKF) is proposed to solve the problems that the Kalman filtering (KF) algorithm easily brings about diffusion when the model becomes a mismatched or noisy, and the MFKF accuracy is reduced when the fading factor is overused. Based on the innovation covariance theory, the algorithm designs an improved basis for judging filtering anomalies and makes the timing of the introduction of the fading factor more reasonable by switching the filtering state. Different from the traditional basis of filter abnormality judgment, the improved judgment basis adopts a recursive way to continuously update the estimated value of the innovation covariance to improve the estimation accuracy of the innovation covariance, and an empirical reserve factor for the judgment basis is introduced to adapt to practical engineering applications. By establishing an inertial navigation system (INS)\/global navigation satellite system (GNSS) integrated navigation model, the results show that the average positioning accuracy of the proposed algorithm is improved by 26.52% and 7.48%, respectively, compared with the KF and MFKF, and shows better robustness and self-adaptability.<\/jats:p>","DOI":"10.3390\/s22145081","type":"journal-article","created":{"date-parts":[[2022,7,7]],"date-time":"2022-07-07T01:15:52Z","timestamp":1657156552000},"page":"5081","source":"Crossref","is-referenced-by-count":8,"title":["Integrated Navigation Algorithm Based on Multiple Fading Factors Kalman Filter"],"prefix":"10.3390","volume":"22","author":[{"given":"Bo","family":"Sun","sequence":"first","affiliation":[{"name":"College of Intelligent Equipment, Shandong University of Science and Technology, Tai\u2019an 271019, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8015-627X","authenticated-orcid":false,"given":"Zhenwei","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, China"}]},{"given":"Shicai","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Intelligent Equipment, Shandong University of Science and Technology, Tai\u2019an 271019, China"}]},{"given":"Xiaobing","family":"Yan","sequence":"additional","affiliation":[{"name":"College of Communication Engineering, Taishan College of Science and Technology, Tai\u2019an 271000, China"}]},{"given":"Chengxu","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Intelligent Equipment, Shandong University of Science and Technology, Tai\u2019an 271019, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Jin, X.B., Robertjeremiah, R., Su, T.L., Bai, J.L., and Kong, J.L. 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